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Director of Data Integrations

Posted 30 days agoViewed

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💎 Seniority level: Director, 8+ years

📍 Location: US

🔍 Industry: Pediatric mental health treatment

🏢 Company: InStride Health👥 101-250💰 $30,000,000 Series B 10 months agoMental HealthWellnessHealth Care

⏳ Experience: 8+ years

🪄 Skills: AWSPythonSQLETLData engineeringData visualizationData modeling

Requirements:
  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
  • 8+ years of experience in data engineering or data integration roles, with at least 3-5 years in a leadership capacity.
  • Proven experience with modern data integration tools, ETL/ELT frameworks, and cloud data platforms.
  • Experience with a wide variety of data sources, including EHRs and CRMs.
  • 3-5 years of experience with data visualization tools such as Sigma or Looker.
  • Deep understanding of data pipeline architectures, data modeling, and ETL best practices.
  • Proficiency with SQL, Python, and familiarity with programming languages such as R.
  • Experience with data quality management and monitoring tools.
  • Familiarity with data governance principles and tools.
  • Strong communication skills to convey technical concepts to non-technical stakeholders.
Responsibilities:
  • Oversee the design, deployment, and maintenance of data pipelines and architecture for high-quality and timely data availability.
  • Lead development and enforcement of data quality standards, governance, and monitoring practices.
  • Act as primary liaison between data engineering and other departments to ensure data needs are met.
  • Gather requirements, prioritize requests, and provide status updates on key projects to keep stakeholders informed.
  • Develop and implement a strategic roadmap for data integrations in alignment with business goals.
  • Assess current capabilities, identify areas for improvement, and ensure integration strategy scales with company growth.
  • Manage and mentor a team of data engineers, fostering a culture of collaboration and continuous improvement.
  • Contribute to the data engineering codebase, especially on high-impact projects, and ensure best practices are followed.
  • Identify and evaluate new data sources for integration into the data ecosystem.
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